Cognitive enterprise system

ABSTRACT

Systems and methods are provided for receiving a query created by a user, receiving output data of at least one function to retrieve data related to the query and analyzing the output data of the at least one function to retrieve data related to the query. The systems and methods further provide for generating at least one dynamic knowledge graph associated with the output data of the at least one function, wherein the at least one dynamic knowledge graph comprises data from the output data of the at least one function and indicates relationships between the data, analyzing the at least one dynamic knowledge graph to determine data relevant to the query generated by the user, and generating a response to the query based on the data relevant in the at least one dynamic knowledge graph.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/424,952, filed Nov. 21, 2016, entitled “COGNITIVE ENTERPRISE,” whichis incorporated herein by reference in its entirety.

BACKGROUND

An enterprise resource planning (ERP) system may provide integratedapplications and technology to automate many back office functionsrelated to technology, services, human resources, and the like. Forexample, ERP software may integrate various functions of an operation,such as product planning, development, manufacturing, sales, andmarketing, in a database application and user interface. Accordingly, anERP system may have a functionally heavy interface against millions ofuser entries.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating a networked system, according tosome example embodiments.

FIG. 2 is a block diagram illustrating the building blocks of acognitive enterprise system, according to some example embodiments.

FIG. 3 is a block diagram illustrating data discovery and knowledge,according to some example embodiments.

FIG. 4 is a flow chart illustrating aspects of a method, according tosome example embodiments, for dynamic intent capturing.

FIG. 5 illustrates an example a user interface for a cognitiveenterprise system, according to some example embodiments.

FIG. 6 is a flow chart illustrating aspects of a method, according tosome example embodiments, for generating a dynamic knowledge graph.

FIGS. 7A and 7B are a block diagram illustrating dynamic knowledge graphand query response generation, according to some example embodiments.

FIG. 8 is a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 9 illustrates a diagrammatic representation of a machine, in theform of a computer system, within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.

DETAILED DESCRIPTION

Systems and methods described herein relate to cognitive enterprisesystems and methods. As explained above, an ERP system may have afunctionally heavy interface against millions of user entries.Embodiments described herein enable ERP to be cognitive, whichsimplifies user interactions in a natural way and breaks down complexityin accessing information for data driven decisions. Moreover,embodiments described herein provide for contextual insights andintelligence processes to bring an end-to-end process beyond ERP to Botor virtual assistant users. In example embodiments, the system learnsfrom user interactions and contextual data to enable and define newbusiness processes and also help make applications (also referred toherein as “apps”) proactive.

Conversational applications are considered the next user interface (UI)paradigm after web and mobile. New chat bot technologies are starting tobecome more prevalent and many entities are evaluating or providing chatbots. Chat bot is a technology service that allows a user to interfacevia a chat interface. A chat bot may be powered by rules, artificialintelligence, and other technologies. There has also been significantresearch in natural language processing and understanding using machinelearning and artificial intelligence, which may be used to developconversational UIs for users. Moreover, smart phones have raisedimplicit expectations for applications to be proactive, such assuggesting a best route based on learnings of individual travelpatterns.

Embodiments described herein relate to cognitive enterprise systems andmethods to address the enterprise domain and fixed knowledge space.Interactions, business implementations, and operations may be flexibleand complex. Also data may be structured, semi-structured, andunstructured.

Chat bots may allow users to interact with enterprise systems in anatural way. There may be a place for conversational apps apart from weband mobile applications. Example embodiments may make it easy to performautomatic processes in simulated conversations. For example, by usingadvances in machine learning and enabling productive apps, regular workmay be easy to perform and productivity increased.

Example embodiments may capture an intent from a natural languagequestion and map the intent to ERP domain objects based on semanticinformation and data elements. Example embodiments may also build adynamic knowledge graph for responses to answer relational questionsbeyond schema definition. Analysis and questions and answers may beperformed on semi-structured data.

FIG. 1 is a block diagram illustrating a networked system 100, accordingto some example embodiments. The system 100 may include one or moreclient devices such as client device 110. The client device 110 maycomprise, but is not limited to, a mobile phone, desktop computer,laptop, portable digital assistants (PDA), smart phone, tablet,Ultrabook, netbook, laptop, multi-processor system, microprocessor-basedor programmable consumer electronic, game console, set-top box, computerin a vehicle, or any other communication device that a user may utilizeto access the networked system 100. In some embodiments, the clientdevice 110 may comprise a display module (not shown) to displayinformation (e.g., in the form of user interfaces). In furtherembodiments, the client device 110 may comprise one or more of touchscreens, accelerometers, gyroscopes, cameras, microphones, globalpositioning system (GPS) devices, and so forth. The client device 110may be a device of a user that is used to create or generate queries.

One or more users 106 may be a person, a machine, or other means ofinteracting with the client device 110. In example embodiments, the user106 may not be part of the system 100, but may interact with the system100 via the client device 110 or other means. For instance, the user 106may provide input (e.g., touch screen input or alphanumeric input) tothe client device 110, and the input may be communicated to otherentities in the system 100 (e.g., third party servers 130, server system102, etc.) via the network 104. In this instance, the other entities inthe system 100, in response to receiving the input from the user 106,may communicate information to the client device 110 via the network 104to be presented to the user 106. In this way, the user 106 may interactwith the various entities in the system 100 using the client device 110.

The system 100 may further include a network 104. One or more portionsof network 104 may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe public switched telephone network (PSTN), a cellular telephonenetwork, a wireless network, a WiFi network, a WiMax network, anothertype of network, or a combination of two or more such networks.

The client device 110 may access the various data and applicationsprovided by other entities in the system 100 via web client 112 (e.g., abrowser, such as the Internet Explorer® browser developed by Microsoft®Corporation of Redmond, Wash. State) or one or more client applications114. The client device 110 may include one or more client applications114 (also referred to as “apps”) such as, but not limited to, a webbrowser, messaging application, electronic mail (email) application, ane-commerce site application, a mapping or location application, acognitive enterprise application, and the like.

In some embodiments, one or more client applications 114 may be includedin a given one of the client devices 110, and configured to locallyprovide the user interface and at least some of the functionalities,with the client application 114 configured to communicate with otherentities in the system 100 (e.g., third party servers 130, server system102, etc.), on an as needed basis, for data and/or processingcapabilities not locally available (e.g., access ERP data, process userqueries, to authenticate a user 106, to verify a method of payment,etc.). Conversely, one or more applications 114 may not be included inthe client device 110, and then the client device 110 may use its webbrowser to access the one or more applications hosted on other entitiesin the system 100 (e.g., third party servers 130, server system 102,etc.).

A server system 102 may provide server-side functionality via thenetwork 104 (e.g., the Internet or wide area network (WAN)) to one ormore third party servers 130 and/or one or more client devices 110. Theserver system 102 may include an application program interface (API)server 120, a web server 122, and a cognitive enterprise server 124,which may be communicatively coupled with one or more databases 126.

The one or more databases 126 may be storage devices that store ERPsystem-related data, user data, and other data. The one or moredatabases 126 may further store information related to third partyservers 130, third party applications 132, client devices 110, clientapplications 114, users 106, and so forth. The one or more databases 126may include cloud-based storage.

The server system 102 may be a cloud computing environment, according tosome example embodiments. The server system 102, and any serversassociated with the server system 102, may be associated with acloud-based application, in one example embodiment.

The cognitive enterprise server 124 may provide back-end support forthird-party applications 132 and client applications 114, which mayinclude cloud-based applications. In one embodiment, the cognitiveenterprise server 124 may receive queries generated by users and processthe queries to generate a response to the queries.

The system 100 may further include one or more third party servers 130.The one or more third party servers 130 may include one or more thirdparty application(s) 132. The one or more third party application(s)132, executing on third party server(s) 130, may interact with theserver system 102 via API server 120 via a programmatic interfaceprovided by the API server 120. For example, one or more the third partyapplications 132 may request and utilize information from the serversystem 102 via the API server 120 to support one or more features orfunctions on a website hosted by the third party or an applicationhosted by the third party. The third party website or application 132,for example, may provide cognitive enterprise functionality that issupported by relevant functionality and data in the server system 102.

FIG. 2 is a block diagram illustrating building blocks 200 of acognitive enterprise system 202, according to some example embodiments.In one example, the server system 102 of FIG. 1 may be a cognitiveenterprise system 202. In another example, the cognitive enterprisesystem 202 may be part of the server system 102.

FIG. 2 shows a web client 112/client application 114 that may interact(e.g., via client device 110) with the cognitive enterprise system 202.The cognitive enterprise system 202 may further interact with an ERPsystem 230 or other enterprise system, in this example.

The cognitive enterprise system 202 may comprise a data discovery module204 that may comprise several modules, such as, an entity extractionmodule 206, an intent extraction module 208, and a data acquisitionmodule 210. The cognitive enterprise system 202 may further comprise aknowledge module 212 that may comprise several modules, such as ERPmetadata module 214, master data module 216, and document informationmodule 218. ERP metadata may comprise metadata from an ERP system (e.g.,ERP system 230). Master data may comprise data from a plurality ofentities (e.g., customers) such as products sold by the entity, namesand other information related to the entity, products procured by theentity, organizations and people within the entity, and so forth.Document information may comprise various documents created by an entityor other party to the enterprise system 202, such as invoices, purchaseorders, and so forth. The knowledge module 212, or knowledge datasource, may contain metadata information from ERP system(s), metadatafrom master data from a plurality of entities, and metadata from variousdocuments. This knowledge data source or knowledge module 212 may beused for data discovery (e.g., by data discovery module 204).

The knowledge data source or knowledge module 212 may comprise dataelement descriptions 302, as shown in FIG. 3. The data elementdescriptions 302 may be used to determine input parameter descriptions304, output parameter descriptions 306, and in out parameterdescriptions 308. This information may be use to map to a particularfunction, such as BAPI_Function_4 310. BAPI is an example of onefunctional interface with metadata. It is understood that otherfunctional interfaces may be used according to some example embodiments.For example, service consumption (e.g., REST APIs) with metadata thatdescribes the relationships may also, or in the alternative, be used,according to some example embodiments.

Returning to FIG. 2, the enterprise system 202 may further comprise ahypertext markup language (HTML) and pipeline orchestration 220 and aquestion and answer (QA) Service 222. The QA server may comprise severalmodules, such as a dynamic knowledge graph module 224 and anargumentative questions module 226. The enterprise system 202 mayfurther comprise third party libraries 228 and a content free grammar(CFG) module 232, an email extraction module 234, a cache service module236, and a conversation intent module 238.

FIG. 4 is a flow chart illustrating aspects of a method 400, accordingto some example embodiments, for processing a query created by a user.For illustrative purposes, method 400 is described with respect to thenetworked system 100 of FIG. 1. It is to be understood that method 400may be practiced with other system configurations in other embodiments.

In operation 402, a server system 102 (e.g., via cognitive enterpriseserver 124) receives a query created by a user. For example, a user 106may use a client device 110 to access an application input a query(e.g., via voice, text, or other means) requesting particularinformation about a product, entity, purchase order, and so forth. FIG.5 illustrates an example a user interface 500 for a cognitive enterprisesystem, according to some example embodiments. In this example, a useris asking a number of questions (502, 506, and 510) about a power drilland the server system 102 is calculating an answer and returning theanswer to be displayed to the user (e.g., 504, 508, 512). For example,the user may input a query 502 that includes “who is the vendor forpower drill 1100 W?” and the server system 102 (e.g., via cognitiveenterprise server 124) may process the query to generate a response 504that includes “The vendor of power drill 1100 w is F1V9, ZE06, ZE05,ZE04, ZE03, ZE02, ZE01.” The user may input follow up queries such asquery 506 asking the location of vendor F1V9 and query 510 asking aboutmaterials for vendor F1V9. The cognitive enterprise server 124 mayprocess each of these queries and generate responses 508 and 512, asdescribed below.

Referring again to FIG. 4, in operation 404, the cognitive enterpriseserver 124 analyzes the query to identify a plurality of elements forthe query using a knowledge data source.

An enterprise domain may comprise of various modules that enablebusiness processes, such as customer relations, procurements, and soforth. The enterprise domain artifacts may comprise of data elementswith descriptions, objects that use instances of data elements withextended purposes, and relationships among these objects. Theseartifacts are ultimately used in business processes. In some exampleembodiments, these relations have been defined over years and exampleembodiments uniquely use these artifacts as one example in a knowledgedata source for understanding a query intent.

In one example, the cognitive enterprise server 124 may tokenize thequery to identify a plurality of elements for the query using aknowledge data source. Using the example query “who are the vendors forpower drill 1100 watts,” the cognitive enterprise server 124 wouldaccess the knowledge data source to identify “vendor” as an element and“power drill 1100 watts” as an element. Other example elements mayinclude a purchase order identifier, a location (e.g., city, state,address, etc.), an invoice identifier, a vendor name, a vendoridentifier, and so forth.

The cognitive enterprise server 124 may then determine, using theknowledge data source, an element type for each element. For example,the element “vendor” may be a vendor type, and the element “power drill1100 watts” may be a product or material type. Each element type may beassociated with an object that comprises various attributes thatdescribes the object. Some other examples of objects may include apurchase order object, a location object, and so forth. For example, apurchase order object may comprise various attributes related to apurchase order, such as a purchase order unique identifier, order date,items (e.g., products, materials, etc.) for the purchase order, and soforth. A location object may include various attributes related to alocation, such as a street address, a city, a state, a country, and soforth. Accordingly, the cognitive enterprise server 124 may determine aplurality of elements and their associated attributes. For example,using the element “power drill 1100 watts,” the cognitive enterpriseserver 124 may determine this element is a product and, thus, whatattributes are associated with a product. The cognitive enterpriseserver 124 may use this to determine other elements in the query.

During the tokenization process, the cognitive enterprise server 124 maycorrect the spelling of the elements in the query. For example, if theword “vendor” is spelled wrong, the cognitive enterprise server 124 mayrevise the element so that the spelling is correct. Also, the cognitiveenterprise server 124 may determine one or more entities associated withthe query. For example, the cognitive enterprise server 124 maydetermine an entity associated with the user, an entity associated withone or more of the plurality of elements (e.g., entity associated with aproduct or material identifier, etc.), and so forth. Accordingly, aspart of the tokenization process, the cognitive enterprise server 124identifies the tokens, corrects spelling, and enriches elements. Theseelements may be used to identify domain specific entities that may beused in functions/services. The cognitive enterprise server may alsolook at the similar words from sources such as dictionaries. Also, basedon these entities the cognitive enterprise server 124 may identifyrelevant data for the query.

In operation 406, the cognitive enterprise server 124 determines whetheradditional elements should be included in the plurality of elements. Inone example, a user 106 may input a word in a query that is not used inthe knowledge data source. For example, the user may input the word“supplier” and the knowledge data source may use the word “vendor.” Inthis example, the cognitive enterprise server 124 may determine that theterm “supplier” is not in the knowledge data source (e.g., not in adictionary of objects). The cognitive enterprise server 124 maydetermine like words to “supplier” (e.g., synonyms). The cognitiveenterprise server 124 may search the knowledge data source for each ofthe synonyms to find one of the synonyms for the element. The cognitiveenterprise server 124 may replace the element with the synonym in theplurality of elements. For example, the cognitive enterprise server 124may replace “supplier” with “vendor.”

In another example, the cognitive enterprise server 124 may usecontext-free grammar (CFG) rules to determine whether additionalelements should be included in the plurality of elements. One example ofa CFG rule may be a rule configured by a user or entity. For example, auser may enter a query for a best vendor for a particular product. Theremay be multiple ways to calculate a best vendor. For example, a vendormay be best if it provides a good lead time, has an advantageous price,based on a minimum order quantity or on price per unit, and so forth. Auser or entity may configure a CFG rule to define best vendor as thevendor that provides the best lead time. Thus, the cognitive enterpriseserver 124 may determine that vendor lead time should be included as anelement in the plurality of elements, based on the CFG rule for bestvendor for that user or the entity associated with that user. The serversystem 102 may, in addition or in the alternative, generate default CFGrules in the event that there is no user or entity defined CFG rule forvarious scenarios. For example, a default rule may be defined for bestvendor as the vendor with the best price per unit. If the cognitiveenterprise server 124 determines that at least one additional elementshould be included in the plurality of elements, the cognitiveenterprise server 124 may add the at least one additional element to theplurality of elements.

In operation 408, the cognitive enterprise server 124 identifies atleast one intent of the query based on the plurality of elements. In oneexample, identifying at least one intent of the query based on theplurality of elements comprises determining at least one input parameterfrom the plurality of elements and determining at least one outputparameter from the plurality of elements. Using the example query “whoare the vendors for power drill 1100 watts,” the cognitive enterpriseserver 124 would determine that “power drill 1100 watts” is an inputparameter (e.g., data provided by the user) and “vendors” is an outputparameter (e.g., an output desired by the user). Input parameters may bevarious terms provided in the query by the user and any additionalelements added to the query. Output parameters may be various termsprovided in the query by the user and any additional elements added tothe query.

For each element of the plurality of elements, the cognitive enterpriseserver 124 may determine whether it is an input parameter or an outputparameter. In one example, the knowledge data source may comprise inputparameter descriptions and output parameter descriptions. The cognitiveenterprise server 124 may access the knowledge data source to determinewhich elements are input parameters and which elements are outputparameters.

In operation 410, the cognitive enterprise server 124 identifies atleast one function to perform to retrieve data associated with theintent of the query. Using a simple example of product name as an inputparameter and vendor name as an output parameter, the cognitiveenterprise server 124 looks for a function that treats the product nameor identifier as an input parameter and gives out the vendor name oridentifier as an output parameter.

In one example, one or more lists or tables of functions may be storedin one or more databases 126. The cognitive enterprise server 124 maycompare at least one input parameter and at least one output parameterfrom the plurality of elements to a list of functions to determine atleast one function that has a function input parameter associated withthe at least one input parameter and a function output parameterassociated with the at least one output parameter. The cognitiveenterprise server 124 may select a function that has a function inputparameter associated with the at least one input parameter and afunction output parameter associated with the at least one outputparameter. The functions in the lists or tables may be services providedby an enterprise system, an entity, and so forth.

In determining the function or functions associated with the intent ofthe query, the cognitive enterprise server 124 may determine if thereare additional parameters defined in the function. In one example, theremay be user or entity defined parameters associated with a function oruser that only allow a particular user access to a particularmanufacturing plant's information. In this example, the cognitiveenterprise server 124 may only look for functions with informationassociated with the particular manufacturing plant, and select afunction with information associated with the particular manufacturingplant. In another example, the function may require additionalparameters that the user did not provide or could not be determined fromthe query. In this example, the cognitive enterprise server 124 may lookfor an alternative function that does not require the unavailableadditional parameters, and select the alternative function.

In operation 412, cognitive enterprise server 124 performs the at leastone function to generate an output of the function. The output of the atleast one function is data relevant to the intent of the query. Thisdata may be output in the form of one or more tables. Using a simpleexample, a function with an input of a vendor identifier may output thevendor information (e.g., vendor name, address, etc.). In someembodiments, there may be multiple functions performed to obtain all ofthe data for the query. In some embodiments, there may be one or moretables comprising the output data for each of the functions performed.

The cognitive enterprise server 124 may next generate a response to thequery based on the output of the function. In one embodiment, thecognitive enterprise server 124 generates one or more dynamic knowledgegraphs comprising the data output by the one or more functions and usesthe one or more dynamic knowledge graphs to generate a response to thequery. FIG. 6 is a flow chart illustrating aspects of a method 600,according to some example embodiments, for generating a dynamicknowledge graph. For illustrative purposes, method 600 is described withrespect to the networked system 100 of FIG. 1. It is to be understoodthat method 600 may be practiced with other system configurations inother embodiments.

In operation 602, the cognitive enterprise server 124 analyzes the dataoutput from the at least one function performed related to the intent ofthe query to generate one or more dynamic knowledge graphs, in operation604. In one example, the output is in the form of a plurality of tables702 as shown in FIG. 7A. FIGS. 7A and 7B comprise one diagram 700, buthave been split into two figures for readability.

The example query 710 shown in FIG. 7B is “Give me the plan id for myvendor north woods, also give me the info record id and purchase org forf1v9.” The input elements in this example are vendor name north woodsand f1v9. The output elements in this example are plant identifier (ID),record ID, and purchase org. In one example, the plant ID may be knownfrom a user or entity profile. Accordingly, the plant ID may be an inputelement instead of an output element. As an example, two functions maybe performed to generate the output data in tables 702 (e.g., the inputelements and output elements may be mapped to two different functions toget the data for the output elements). One example function may beBAPI_INFORECORD_GETLIST and another example function may beBAPI_PO_GETITEMS.

The tables 702 shown in FIG. 7A have been simplified and reduced tothree example tables 704, 706, and 708 for purposes of illustration. Theoutput from the at least one function may comprise many more tables,tables with many more rows and columns, fewer tables, and so forth.

In one example, there is no semantic relevance defined between theplurality of tables. Accordingly, the cognitive enterprise server 124generates one or more knowledge graphs based on the tables with theentities and relationships between the entities. For example, thecognitive enterprise server 124 may generate dynamic knowledge graphs712, 714, and 716, based on the tables 702.

Moreover, the data in the tables is normalized with respect to the typeof data in the tables. For instance, the data may come from differentsources with different naming conventions. In one example, one sourcemay define a column as VendorID, another source may define the samecolumn as Vendor Number, another Vendor Identifier, and so forth. Theserver system 102 may normalize the data with respect to all of thenaming conventions so that even though different tables have differentnaming conventions, the naming conventions will be normalized to allrefer to the same naming convention. For example, the table column willbe VendorID even though the underlying naming conventions may be VendorNumber, Vendor Identifier, or other naming convention.

In one example, the dynamic knowledge graphs may be generated startingwith a vendorID, as shown in table 704. For example, the cognitiveenterprise server 124 may generate a first node of a dynamic knowledgegraph with the vendor ID F1V9, as shown in the dynamic knowledge graph712. The cognitive enterprise server 124 may then create a separate nodefor each element in the table 704, such as VendorName Appawa Controlsand PurchaseOrderID 4500000149. The cognitive enterprise server 124 maythen analyze the other tables to see what other data is relevant to thenodes already created. The cognitive enterprise server 124 may identifytable 706 as having a VendorID column with the VendorID F1V9. Thecognitive enterprise server 124 may then add data from table 706 asindividual nodes to the dynamic knowledge graph. For example, thecognitive enterprise server 124 would add a node for City Colorado andPurchaseOrganization E001. In this way, each relevant element of each ofthe tables becomes a node in the dynamic knowledge graph. The arrowsshow the relationships between the nodes.

The cognitive enterprise server 124 may then analyze the other tables tosee what other data is relevant to the nodes already created. Thecognitive enterprise server 124 may identify table 708 as having aPurchaseOrganization column with the PurchaseOrganization E001. Thecognitive enterprise server 124 may then add data from table 708 to thedynamic knowledge graph. For example, the cognitive enterprise server124 would add a node for InfoRecordID 530000042 and PlantID P001. Thecognitive enterprise server 124 may continue analyzing tables untilthere are no more tables with relevant data. The resulting dynamicknowledge graph for the above example is shown as dynamic knowledgegraph 712.

The cognitive enterprise server 124 would generate knowledge graphs 714and 716 in a similar manner as described above with respect to dynamicknowledge graph 712.

Returning to FIG. 6, in operation 606, the cognitive enterprise server124 analyzes the at least one dynamic graph to determine data togenerate a response to the query input by a user. For example, thecognitive enterprise server 124 pulls the plant ID from the dynamicknowledge graph 716 (the node with PlantID) and the record ID andpurchase organization from the dynamic knowledge graph 712 to generatethe response 718. In operation 608, the cognitive enterprise server 124generates a response to the query based on the data from the at leastone dynamic graph. The response is provided to a user 106, as shown inoperation 610. For example, the response is provided to computing device(e.g., client 110) associated with a user 106, to be displayed to theuser 106.

The dynamic knowledge graphs shown in FIG. 7B may then be used foradditional queries generated by the user 106. Moreover, the dynamicknowledge graphs may be incrementally built with further queries and/ormore dynamic knowledge graphs may be generated. This may continue whilethe context of the queries are similar or associated. Once the contextof the queries changes, the graphs will be discarded and new graphs willbe generated for the new query in the new context.

In one example, the cognitive enterprise server 124 generates dynamicknowledge graphs for a first query (as described above). The cognitiveenterprise server 124 receives a second query and determines that thesecond query is in the same context of the first query and can eithersimply use the dynamic knowledge graphs generated for the first query,or can add to the graphs to fully answer the second query. The cognitiveenterprise server 124 may continue this process until it determines thata query is not in the same context as the prior query. The dynamicknowledge graphs may be deleted once they are no longer needed (e.g.,when a query is no longer in the same context as a prior query).

In one example, a query may have two elements. A first element may be aninstance and a second element may be an attribute. For example, in thequery “what is the vendor id of power drill 1100 w,” power drill 1100 wmay be the instance and vendor id may be the attribute. For this query,these are saved in a dynamic knowledge graph. If in the following query,one of these elements is missing, the system may obtain the context fromthe previous memory state saved.

To search through and pull information through an entire informationspace to get data for a response to a query would require significantfootprint and be incredibly time consuming. Generating and utilizing adynamic knowledge graph instead is extremely fast and takes less storagespace and processing power. The specific data necessary is generated andthen the knowledge graphs are created dynamically. This results in amore efficient system because of less storage and processingrequirements and a faster response to queries.

Example embodiments of the cognitive enterprise systems may provide forsystems and methods for conversational interaction, context,personalization, cognitive functionality, and simplification. Exampleembodiments may provide for intent capturing from a natural languagequestion to ERP domain objects based on semantic information and dataelements, building a dynamic knowledge graph for responses to answerrelational questions beyond schema definition, and QA on semi-structureddata.

FIG. 8 is a block diagram 800 illustrating software architecture 802,which can be installed on any one or more of the devices describedabove. For example, in various embodiments, client devices 110 andserver systems 130, 102, 120, 122, and 124 may be implemented using someor all of the elements of software architecture 802. FIG. 8 is merely anon-limiting example of a software architecture, and it will beappreciated that many other architectures can be implemented tofacilitate the functionality described herein. In various embodiments,the software architecture 802 is implemented by hardware such as machine900 of FIG. 9 that includes processors 910, memory 930, and I/Ocomponents 950. In this example, the software architecture 802 can beconceptualized as a stack of layers where each layer may provide aparticular functionality. For example, the software architecture 802includes layers such as an operating system 804, libraries 806,frameworks 808, and applications 810. Operationally, the applications810 invoke API calls 812 through the software stack and receive messages814 in response to the API calls 812, consistent with some embodiments.

In various implementations, the operating system 804 manages hardwareresources and provides common services. The operating system 804includes, for example, a kernel 820, services 822, and drivers 824. Thekernel 820 acts as an abstraction layer between the hardware and theother software layers, consistent with some embodiments. For example,the kernel 820 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 822 can provide other commonservices for the other software layers. The drivers 824 are responsiblefor controlling or interfacing with the underlying hardware, accordingto some embodiments. For instance, the drivers 824 can include displaydrivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers,flash memory drivers, serial communication drivers (e.g., UniversalSerial Bus (USB) drivers), WI-FI® drivers, audio drivers, powermanagement drivers, and so forth.

In some embodiments, the libraries 806 provide a low-level commoninfrastructure utilized by the applications 810. The libraries 806 caninclude system libraries 830 (e.g., C standard library) that can providefunctions such as memory allocation functions, string manipulationfunctions, mathematic functions, and the like. In addition, thelibraries 806 can include API libraries 832 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as Moving Picture Experts Group-4 (MPEG4), AdvancedVideo Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3),Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec,Joint Photographic Experts Group (JPEG or JPG), or Portable NetworkGraphics (PNG)), graphics libraries (e.g., an OpenGL framework used torender in two dimensions (2D) and in three dimensions (3D) graphiccontent on a display), database libraries (e.g., SQLite to providevarious relational database functions), web libraries (e.g., WebKit toprovide web browsing functionality), and the like. The libraries 806 canalso include a wide variety of other libraries 834 to provide many otherAPIs to the applications 810.

The frameworks 808 provide a high-level common infrastructure that canbe utilized by the applications 810, according to some embodiments. Forexample, the frameworks 808 provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 808 can provide a broad spectrum of otherAPIs that can be utilized by the applications 810, some of which may bespecific to a particular operating system 804 or platform.

In an example embodiment, the applications 810 include a homeapplication 850, a contacts application 852, a browser application 854,a book reader application 856, a location application 858, a mediaapplication 860, a messaging application 862, a game application 864,and a broad assortment of other applications such as a third partyapplications 866. According to some embodiments, the applications 810are programs that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications 810, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, the third party application 866 (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the third party application 866 can invoke the API calls 812provided by the operating system 804 to facilitate functionalitydescribed herein.

Some embodiments may particularly include a cognitive enterpriseapplication 867. In certain embodiments, this may be a stand-aloneapplication that operates to manage communications with a server systemsuch as third party servers 130 or server system 102. In otherembodiments, this functionality may be integrated with anotherapplication. The cognitive enterprise application 867 may request anddisplay various data related to ERP or other system and may provide thecapability for a user 106 to input data related to the system via atouch interface, keyboard, or using a camera device of machine 900,communication with a server system via I/O components 950, and receiptand storage of object data in memory 930. Presentation of informationand user inputs associated with the information may be managed bysoftware version analysis application 867 using different frameworks808, library 806 elements, or operating system 804 elements operating ona machine 1300.

The following examples describe various embodiments of methods,machine-readable media, and systems (e.g., machines, devices, or otherapparatus) discussed herein.

EXAMPLE 1

A method comprising:

receiving, at a server computer, a query created by a user;

receiving, at the server computer, output data of at least one functionto retrieve data related to the query;

analyzing, by the one or more hardware processors of the servercomputer, the output data of the at least one function to retrieve datarelated to the query;

generating, by the one or more hardware processors of the servercomputer, at least one dynamic knowledge graph associated with theoutput data of the at least one function, wherein the at least onedynamic knowledge graph comprises data from the output data of the atleast one function and indicates relationships between the data;

analyzing, by the one or more hardware processors of the servercomputer, the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

generating, by the one or more hardware processors of the servercomputer, a response to the query based on the data relevant in the atleast one dynamic knowledge graph; and

providing, by the one or more hardware processors of the servercomputer, the response to the input query to a computing deviceassociated with the user.

EXAMPLE 2

A method according to any of the previous examples, wherein the querycreated by the user is a first query, and the method further comprising:

receiving a second query created by the user;

analyzing the query to determine that the query is in a same context ofthe first query;

analyzing the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

generating a response to the query based on the data relevant in the atleast one dynamic knowledge graph; and

providing the response to the input query to a computing deviceassociated with the user.

EXAMPLE 3

A method according to any of the previous examples, wherein the querycreated by the user is a first query, and the method further comprising:

receiving a second query created by the user;

analyzing the query to determine that the query is in a same context ofthe first query;

analyzing the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

determining additional data for a response to the query that is notincluded in the at least one dynamic knowledge graph;

incrementing the at least one dynamic knowledge graph to include theadditional data needed for a response to the query based on output datafrom at least one additional function;

generating a response to the query based on the data in the at least onedynamic knowledge graph that has been incremented to include theadditional data needed for a response to the query based on output datafrom at least one additional function; and

providing the response to the input query to a computing deviceassociated with the user.

EXAMPLE 4

A method according to any of the previous examples, wherein the querycreated by the user is a first query, and the method further comprising:

receiving a second query created by the user;

analyzing the query to determine that the query is not in a same contextof the first query; and

discarding the at least one dynamic knowledge graph.

EXAMPLE 5

A method according to any of the previous examples, wherein output dataof at least one function to retrieve data related to the query is in theform of one or more tables.

EXAMPLE 6

A method according to any of the previous examples, wherein there is nosemantic relevance defined between the plurality of tables.

EXAMPLE 7

A method according to any of the previous examples, wherein data fromthe output data of the at least one function is represented as nodes inthe dynamic knowledge graph.

EXAMPLE 8

A method according to any of the previous examples, wherein the query iscreated by a user in an enterprise resource planning (ERP) system.

EXAMPLE 9

A server computer comprising:

one or more hardware processors; and

a computer-readable medium coupled with the one or more hardwareprocessors, the computer-readable medium comprising instructions storedthereon that are executable by the one or more hardware processors tocause the server computer to perform operations comprising:

-   -   receiving a query created by a user;    -   receiving output data of at least one function to retrieve data        related to the query;    -   analyzing the output data of the at least one function to        retrieve data related to the query;    -   generating at least one dynamic knowledge graph associated with        the output data of the at least one function, wherein the at        least one dynamic knowledge graph comprises data from the output        data of the at least one function and indicates relationships        between the data;    -   analyzing the at least one dynamic knowledge graph to determine        data relevant to the query generated by the user;    -   generating a response to the query based on the data relevant in        the at least one dynamic knowledge graph; and    -   providing the response to the input query to a computing device        associated with the user.

EXAMPLE 10

A server computer according to any of the previous examples, wherein thequery created by the user is a first query, and the operations furthercomprising:

receiving a second query created by the user;

analyzing the query to determine that the query is in a same context ofthe first query;

analyzing the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

generating a response to the query based on the data relevant in the atleast one dynamic knowledge graph; and

providing the response to the input query to a computing deviceassociated with the user.

EXAMPLE 11

A server computer according to any of the previous examples, wherein thequery created by the user is a first query, and the operations furthercomprising:

receiving a second query created by the user;

analyzing the query to determine that the query is in a same context ofthe first query;

analyzing the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

determining additional data for a response to the query that is notincluded in the at least one dynamic knowledge graph;

incrementing the at least one dynamic knowledge graph to include theadditional data needed for a response to the query based on output datafrom at least one additional function;

generating a response to the query based on the data in the at least onedynamic knowledge graph that has been incremented to include theadditional data needed for a response to the query based on output datafrom at least one additional function; and

providing the response to the input query to a computing deviceassociated with the user.

EXAMPLE 12

A server computer according to any of the previous examples, wherein thequery created by the user is a first query, and the operations furthercomprising:

receiving a second query created by the user;

analyzing the query to determine that the query is not in a same contextof the first query; and

discarding the at least one dynamic knowledge graph.

EXAMPLE 13

A server computer according to any of the previous examples, whereinoutput data of at least one function to retrieve data related to thequery is in the form of one or more tables.

EXAMPLE 14

A server computer according to any of the previous examples, whereinthere is no semantic relevance defined between the plurality of tables.

EXAMPLE 15

A server computer according to any of the previous examples, whereindata from the output data of the at least one function is represented asnodes in the dynamic knowledge graph.

EXAMPLE 16

A server computer according to any of the previous examples, wherein thequery is created by a user in an enterprise resource planning (ERP)system.

EXAMPLE 17

A non-transitory computer-readable medium comprising instructions storedthereon that are executable by at least one processor to cause acomputing device to perform operations comprising:

receiving a query created by a user;

receiving output data of at least one function to retrieve data relatedto the query;

analyzing the output data of the at least one function to retrieve datarelated to the query;

generating at least one dynamic knowledge graph associated with theoutput data of the at least one function, wherein the at least onedynamic knowledge graph comprises data from the output data of the atleast one function and indicates relationships between the data;

analyzing the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

generating a response to the query based on the data relevant in the atleast one dynamic knowledge graph; and

providing the response to the input query to a computing deviceassociated with the user.

EXAMPLE 18

A non-transitory computer-readable medium according to any of theprevious examples, wherein the query created by the user is a firstquery, and the operations further comprising:

receiving a second query created by the user;

analyzing the query to determine that the query is in a same context ofthe first query;

analyzing the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

generating a response to the query based on the data relevant in the atleast one dynamic knowledge graph; and

providing the response to the input query to a computing deviceassociated with the user.

EXAMPLE 19

A non-transitory computer-readable medium according to any of theprevious examples, wherein the query created by the user is a firstquery, and the operations further comprising:

receiving a second query created by the user;

analyzing the query to determine that the query is in a same context ofthe first query;

analyzing the at least one dynamic knowledge graph to determine datarelevant to the query generated by the user;

determining additional data for a response to the query that is notincluded in the at least one dynamic knowledge graph;

incrementing the at least one dynamic knowledge graph to include theadditional data needed for a response to the query based on output datafrom at least one additional function;

generating a response to the query based on the data in the at least onedynamic knowledge graph that has been incremented to include theadditional data needed for a response to the query based on output datafrom at least one additional function; and

providing the response to the input query to a computing deviceassociated with the user.

EXAMPLE 20

A non-transitory computer-readable medium according to any of theprevious examples, wherein the query created by the user is a firstquery, and the operations further comprising:

receiving a second query created by the user;

analyzing the query to determine that the query is not in a same contextof the first query; and

discarding the at least one dynamic knowledge graph.

FIG. 9 is a block diagram illustrating components of a machine 900,according to some embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions916 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein can be executed. Inalternative embodiments, the machine 900 operates as a standalone deviceor can be coupled (e.g., networked) to other machines. In a networkeddeployment, the machine 900 may operate in the capacity of a servermachine 130, 102, 120, 122, 124, and the like, or a client device 110 ina server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 900 cancomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a personal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 916, sequentially or otherwise, that specify actions to betaken by the machine 900. Further, while only a single machine 900 isillustrated, the term “machine” shall also be taken to include acollection of machines 900 that individually or jointly execute theinstructions 916 to perform any one or more of the methodologiesdiscussed herein.

In various embodiments, the machine 900 comprises processors 910, memory930, and I/O components 950, which can be configured to communicate witheach other via a bus 902. In an example embodiment, the processors 910(e.g., a central processing unit (CPU), a reduced instruction setcomputing (RISC) processor, a complex instruction set computing (CISC)processor, a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), another processor, or anysuitable combination thereof) include, for example, a processor 912 anda processor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors 910 that maycomprise two or more independent processors 912, 914 (also referred toas “cores”) that can execute instructions 916 contemporaneously.Although FIG. 9 shows multiple processors 910, the machine 900 mayinclude a single processor 910 with a single core, a single processor910 with multiple cores (e.g., a multi-core processor 910), multipleprocessors 912, 914 with a single core, multiple processors 912, 914with multiples cores, or any combination thereof.

The memory 930 comprises a main memory 932, a static memory 934, and astorage unit 936 accessible to the processors 910 via the bus 902,according to some embodiments. The storage unit 936 can include amachine-readable medium 938 on which are stored the instructions 916embodying any one or more of the methodologies or functions describedherein. The instructions 916 can also reside, completely or at leastpartially, within the main memory 932, within the static memory 934,within at least one of the processors 910 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 900. Accordingly, in various embodiments, themain memory 932, the static memory 934, and the processors 910 areconsidered machine-readable media 938.

As used herein, the term “memory” refers to a machine-readable medium938 able to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 938 is shown, in an example embodiment, to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 916. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing instructions (e.g., instructions 916) for executionby a machine (e.g., machine 900), such that the instructions 916, whenexecuted by one or more processors of the machine 900 (e.g., processors910), cause the machine 900 to perform any one or more of themethodologies described herein. Accordingly, a “machine-readable medium”refers to a single storage apparatus or device, as well as “cloud-based”storage systems or storage networks that include multiple storageapparatus or devices. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, one or more datarepositories in the form of a solid-state memory (e.g., flash memory),an optical medium, a magnetic medium, other non-volatile memory (e.g.,erasable programmable read-only memory (EPROM)), or any suitablecombination thereof. The term “machine-readable medium” specificallyexcludes non-statutory signals per se.

The I/O components 950 include a wide variety of components to receiveinput, provide output, produce output, transmit information, exchangeinformation, capture measurements, and so on. In general, it will beappreciated that the I/O components 950 can include many othercomponents that are not shown in FIG. 9. The I/O components 950 aregrouped according to functionality merely for simplifying the followingdiscussion, and the grouping is in no way limiting. In various exampleembodiments, the I/O components 950 include output components 952 andinput components 954. The output components 952 include visualcomponents (e.g., a display such as a plasma display panel (PDP), alight emitting diode (LED) display, a liquid crystal display (LCD), aprojector, or a cathode ray tube (CRT)), acoustic components (e.g.,speakers), haptic components (e.g., a vibratory motor), other signalgenerators, and so forth. The input components 954 include alphanumericinput components (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and force of touches or touch gestures, orother tactile input components), audio input components (e.g., amicrophone), and the like.

In some further example embodiments, the I/O components 950 includebiometric components 956, motion components 958, environmentalcomponents 960, or position components 962, among a wide array of othercomponents. For example, the biometric components 956 include componentsto detect expressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram based identification), and the like. The motioncomponents 958 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components960 include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensor components (e.g., machine olfactiondetection sensors, gas detection sensors to detect concentrations ofhazardous gases for safety or to measure pollutants in the atmosphere),or other components that may provide indications, measurements, orsignals corresponding to a surrounding physical environment. Theposition components 962 include location sensor components (e.g., aGlobal Positioning System (GPS) receiver component), altitude sensorcomponents (e.g., altimeters or barometers that detect air pressure fromwhich altitude may be derived), orientation sensor components (e.g.,magnetometers), and the like.

Communication can be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 or devices 970 via a coupling982 and a coupling 972, respectively. For example, the communicationcomponents 964 include a network interface component or another suitabledevice to interface with the network 980. In further examples,communication components 964 include wired communication components,wireless communication components, cellular communication components,near field communication (NFC) components, BLUETOOTH® components (e.g.,BLUETOOTH® Low Energy), WI-FI® components, and other communicationcomponents to provide communication via other modalities. The devices970 may be another machine 900 or any of a wide variety of peripheraldevices (e.g., a peripheral device coupled via a Universal Serial Bus(USB)).

Moreover, in some embodiments, the communication components 964 detectidentifiers or include components operable to detect identifiers. Forexample, the communication components 964 include radio frequencyidentification (RFID) tag reader components, NFC smart tag detectioncomponents, optical reader components (e.g., an optical sensor to detecta one-dimensional bar codes such as a Universal Product Code (UPC) barcode, multi-dimensional bar codes such as a Quick Response (QR) code.Aztec Code, Data Matrix. Dataglyph, MaxiCode, PDF417, Ultra Code,Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes,and other optical codes), acoustic detection components (e.g.,microphones to identify tagged audio signals), or any suitablecombination thereof. In addition, a variety of information can bederived via the communication components 964, such as location viaInternet Protocol (IP) geo-location, location via WI-FI® signaltriangulation, location via detecting a BLUETOOTH® or NFC beacon signalthat may indicate a particular location, and so forth.

In various example embodiments, one or more portions of the network 980can be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the publicswitched telephone network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a WI-FI®network, another type of network, or a combination of two or more suchnetworks. For example, the network 980 or a portion of the network 980may include a wireless or cellular network, and the coupling 982 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 982 can implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

In example embodiments, the instructions 916 are transmitted or receivedover the network 980 using a transmission medium via a network interfacedevice (e.g., a network interface component included in thecommunication components 964) and utilizing any one of a number ofwell-known transfer protocols (e.g., Hypertext Transfer Protocol(HTTP)). Similarly, in other example embodiments, the instructions 916are transmitted or received using a transmission medium via the coupling972 (e.g., a peer-to-peer coupling) to the devices 970. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying the instructions 916for execution by the machine 900, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such software.

Furthermore, the machine-readable medium 938 is non-transitory (in otherwords, not having any transitory signals) in that it does not embody apropagating signal. However, labeling the machine-readable medium 938“non-transitory” should not be construed to mean that the medium isincapable of movement, the medium 938 should be considered as beingtransportable from one physical location to another. Additionally, sincethe machine-readable medium 938 is tangible, the medium 938 may beconsidered to be a machine-readable device.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: receiving, by one or morehardware processors of a server computer, a query created by a user;performing, by the one or more processors of the server computer, atleast one function to retrieve data associated with an intent of thequery, the at least one function generating a plurality of tablescomprising output data related to the query; analyzing, by the one ormore processors of the server computer, a first table of the pluralityof tables to generate a first node of a dynamic knowledge graph, thefirst node comprising a first data type and the dynamic knowledge graphindicating relationships between nodes of the dynamic knowledge graph;generating, by the one or more processors of the server computer, aseparate node of the dynamic knowledge graph for each element in thefirst table corresponding to the first data type; for each table of theother tables in the plurality of tables: analyzing the table todetermine whether the table comprises a data type corresponding to apreviously generated node in the dynamic knowledge graph; based ondetermining that the table comprises a data type corresponding topreviously generated node in the dynamic knowledge graph, generating aseparate node for each element in the table corresponding to the datatype of the previously generated node in the dynamic knowledge graph;analyzing, by the one or more hardware processors of the servercomputer, the dynamic knowledge graph to determine data relevant to thequery generated by the user; generating, by the one or more hardwareprocessors of the server computer, a response to he query based on thedata relevant in the dynamic knowledge graph; and providing, by the oneor more hardware processors of the server computer, the response to theinput query to a computing device associated with the user.
 2. Themethod of claim 1, wherein the query created by the user is a firstquery, and the method further comprising: receiving a second querycreated by the user; analyzing the second query to determine that thesecond query is in a same context of the first query; analyzing thedynamic knowledge graph to determine data relevant to the second querygenerated by the user; generating a response to the second query basedon the data relevant in the dynamic knowledge graph; and providing theresponse to the second query to a computing device associated with theuser.
 3. The method of claim 1, wherein the query created by the user isa first query, and the method further comprising: receiving a secondquery created by the user; analyzing the second query to determine thatthe second query is in a same context of the first query; analyzing thedynamic knowledge graph to determine data relevant to the second querygenerated by the user; determining additional data for a response to thequery that is not included in the dynamic knowledge graph; incrementingthe dynamic knowledge graph to include the additional data needed for aresponse to the second query based on output data from at least oneadditional function; generating a response to the second query based onthe data in the dynamic knowledge graph that has been incremented toinclude the additional data needed for a response to the second querybased on output data from at least one additional function; andproviding the response to the second query to a computing deviceassociated with the user.
 4. The method of claim 1, wherein the querycreated by the user is a first query, and the method further comprising:receiving a second query created by the user; analyzing the second queryto determine that the second query is in a new context that is not in asame context of the first query; discarding the at least one dynamicknowledge graph; and generating a new dynamic knowledge graph for thesecond query in the new context.
 5. The method of claim 1, wherein thereis no semantic relevance defined between the plurality of tables.
 6. Themethod of claim 1, wherein the query is created by a user in anenterprise resource planning (ERP) system.
 7. The method of claim 1,wherein the dynamic knowledge graph is a first dynamic knowledge graph,and wherein the first node of the dynamic knowledge graph comprises afirst data value of a first data type.
 8. The method of claim 7, furthercomprising: analyzing the first table of the plurality of tables togenerate a first node of a second dynamic knowledge graph, the secondnode comprising a second data value of the first data type and thesecond dynamic knowledge graph indicating relationships between nodes ofthe second dynamic knowledge graph; generating a separate node of thesecond dynamic knowledge graph for each element in the first tablecorresponding to the second data value of the first data type; for eachtable of the other tables in the plurality of tables: analyzing thetable to determine whether the table comprises a data valuecorresponding to a previously generated node in the second dynamicknowledge graph; and based on determining that the table comprises adata value corresponding to previously generated node in the seconddynamic knowledge graph, generating a separate node for each element inthe table corresponding to the data value of the previously generatednode in the second dynamic knowledge graph.
 9. The method of claim 8,the server computer analyzes first dynamic knowledge graph and thesecond dynamic knowledge graph determine data relevant to the querygenerated by the use and generates the response to the query based onthe data relevant in the first dynamic knowledge graph and the seconddynamic knowledge graph.
 10. The method of claim 9, wherein the firstdynamic knowledge graph and the second dynamic knowledge graph arediscarded once a new query is received that is in a context differentthan the context of the first dynamic knowledge graph and the seconddynamic knowledge graph.
 11. A server computer: one or more hardwareprocessors; and a computer-readable medium coupled with the one or morehardware processors, the computer-readable medium comprisinginstructions stored thereon that are executable by the one or morehardware processors to cause the server computer to perform operationscomprising: receiving a query created by a user; performing at least onefunction to retrieve data associated with an intent of the query, the atleast one function generating a plurality of tables comprising outputdata related to the query; analyzing a first table of the plurality oftables to generate a first node of a dynamic knowledge graph, the firstnode comprising a first data type and the dynamic knowledge graphindicating relationships between nodes of the dynamic knowledge graph;generating a separate node of the dynamic knowledge graph for eachelement in the first table corresponding to the first data type; foreach table of the other tables in the plurality of tables: analyzing thetable to determine whether the table comprises a data type correspondingto a previously generated node in the dynamic knowledge graph; based ondetermining that the table comprises a data type corresponding topreviously generated node in the dynamic knowledge graph, generating aseparate node for each element in the table corresponding to the datatype of the previously generated node in the dynamic knowledge graph;analyzing the dynamic knowledge graph to determine data relevant to thequery generated by the user; generating a response to the query based onthe data relevant in the dynamic knowledge graph; and providing theresponse to the input query to a computing device associated with theuser.
 12. The server computer of claim 11, wherein the query created bythe user is a first query, and the operations further comprising:receiving a second query created by the user; analyzing the second queryto determine that the second query is in a same context of the firstquery; analyzing the dynamic knowledge graph to determine data relevantto the second query generated by the user; generating a response to thesecond query based on the data relevant in the dynamic knowledge graph;and providing the response to the second query to a computing deviceassociated with the user.
 13. The server computer of claim 11, whereinthe query created by the user is a first query, and the operationsfurther comprising: receiving a second query created by the user;analyzing the second query to determine that the second query is in asame context of the first query; analyzing the dynamic knowledge graphto determine data relevant to the second query generated by the user;determining additional data for a response to the query that is notincluded in the dynamic knowledge graph; incrementing the dynamicknowledge graph to include the additional data needed for a response tothe second query based on output data from at least one additionalfunction; generating a response to the second query based on the data inthe dynamic knowledge graph that has been incremented to include theadditional data needed for a response to the second query based onoutput data from at least one additional function; and providing theresponse to the second query to a computing device associated with tuser.
 14. The server computer of claim 11, wherein the query created bythe user is a first query, and the operations further comprising:receiving a second query created by the user; analyzing the second queryto determine that the second query is in a new context that is not in asame context of the first query; discarding the at least one dynamicknowledge graph; and generating a new dynamic knowledge graph for thesecond query in the new context.
 15. The server computer of claim 11,wherein there is no semantic relevance defined between the plurality oftables.
 16. The server computer of claim 11, wherein the query iscreated by a user in an enterprise resource planning (ERP) system.
 17. Anon-transitory computer-readable medium comprising instructions storedthereon that are executable by at least one processor to cause acomputing device to perform operations comprising: receiving a querycreated by a user; performing at least one function to retrieve dataassociated with an intent of the query, the at least one functiongenerating a plurality of tables comprising output data related to thequery; analyzing a first table of the plurality of tables to generate afirst node of a dynamic knowledge graph, the first node comprising afirst data type and the dynamic knowledge graph indicating relationshipsbetween nodes of the dynamic knowledge graph; generating a separate nodeof the dynamic knowledge graph for each element in the first tablecorresponding to the first data type; for each table of the other tablesin the plurality of tables: analyzing the table to determine whether thetable comprises a data type corresponding to a previously generated nodein the dynamic knowledge graph; based on determining that the tablecomprises a data type corresponding to previously generated node in thedynamic knowledge graph, generating a separate node for each element inthe table corresponding to the data type of the previously generatednode in the dynamic knowledge graph: analyzing the dynamic knowledgegraph to determine data relevant to the query generated by the user;generating a response to the query based on the data relevant in thedynamic knowledge graph; and providing the response to the input queryto a computing device associated with the user.
 18. The non-transitorycomputer-readable medium of claim 17, wherein the query created by theuser is a first query, and the operations further comprising: receivinga second query created by the user; analyzing the second query todetermine that the second query is in a same context of the first query;analyzing the dynamic knowledge graph to determine data relevant to thesecond query generated by the user; generating a response to the secondquery based on the data relevant in the dynamic knowledge graph; andproviding the response to the second query to a computing deviceassociated with the user.
 19. The non-transitory computer-readablemedium of claim 17, wherein the query created by the user is a firstquery, and the operations further comprising: receiving a second querycreated by the user; analyzing the second query to determine that thesecond query is in a same context of the first query; analyzing thedynamic knowledge graph to determine data relevant to the second querygenerated by the user; determining additional data for a response to thequery that is not included in the dynamic knowledge graph; incrementingthe dynamic knowledge graph to include the additional data needed for aresponse to the second query based on output data from at least oneadditional function; generating a response to the second query based onthe data in the dynamic knowledge graph that has been incremented toinclude the additional data needed for a response to the second querybased on output data from at least one additional function; andproviding the response to the second query to a computing deviceassociated with user.
 20. The non-transitory computer-readable medium ofclaim 17, wherein the query created by the user is a first query, andthe operations further comprising: receiving a second query created bythe user; analyzing the second query to determine that the second queryis in a new context that is not in a same context of the first query;discarding the at least one dynamic knowledge graph; and generating anew dynamic knowledge graph for the second query in the new context.